Climate change and its consequences were increasingly being recognized as among the most significant challenges of our time, yet there was considerable uncertainty regarding the social and environmental impacts because the predictive potential of numerical models of the Earth system was limited. There was a clear need to develop improved assessments of climate change, including but not limited to global and regional changes, extreme events and stresses on environment and society, and a comprehensive characterization and/or reduction of uncertainty.

Climate and earth sciences have recently experienced a rapid transformation from a data-poor to a data-rich environment. In particular, climate related observations from remote sensors on satellites and weather radars, or from in situ sensors and sensor networks, as well as outputs of climate or Earth system models from large-scale computational platforms, provided terabytes of temporal, spatial and spatio-temporal data. In addition, the rapid growth of geographical information systems lead to the availability of multi-source data. These massive and information rich datasets offered a huge potential for advancing the science of climate change and impacts. This workshop brought together researchers who are advancing computational and data analysis methods necessary to address the key challenges in climate change science. A major focus of the workshop was on computational data science tools that could extract the achievable predictive insights from climate data and capture the complex dependence structures among climate variables.

The workshop was held at the University of Minnesota in Minneapolis. The program included invited talks by leading experts in the field, panel discussions, and poster sessions. Additional details can be found on the workshop program.

Download the Workshop Poster (pdf)